In the quest for new business opportunities, there has been a growing proliferation of products and services seeking to more relevantly satisfy consumer needs. This has heightened competition and furthered a desire by marketers to look for tools that can more precisely identify optimal groups of consumers. Previous targeting methods used historical information to determine what type of consumer had previously used product/service categories or brands. These factors were used to predict which consumers would likely buy in the future.
Previous approaches to target marketing prioritized consumers based on category and volume of brand usage. These consumer targeting efforts were largely based on demographic and geodemographic factors. A first approach typically involved the administration of a survey to measure consumer usage levels pertaining to specific products, services and brands. These surveys also gathered general demographic information for each respondent. Standard analysis techniques were then applied to study the results and identify optimal demographic segments for targeting marketing efforts. Geodemographic systems were then developed that categorize the entire marketplace of consumers into a specific number of neighborhood types. These neighborhood types were typically classified according to demographic factors.
Unfortunately, targeting methods based on demographics and geodemographics have several drawbacks. For example, both methods assume that all consumers within a defined demographic or geodemographic sub-set are equally attractive. As such, these methods typically do not distinguish between individual consumers within the same group. In addition, neither method considers attitudinal variables, even though attitudinal variables greatly influence the future purchasing behavior of consumers. Because of these drawbacks, volume-only marketing techniques often do not meet the financial needs of marketers. Additionally, there has also been a growing consensus that demographic and other conventional targeting methodologies would be enhanced if attitudinal filtering were also applied. In response to this, businesses are increasingly striving to find ways of identifying and reaching groups of consumers who tend to “think alike” with respect to their brand and market segment. Some examples of groups divided based on attitudinal variables are:                Early adopters of high tech consumer products;        Risk-averse buyers of investment securities;        Prestige-seeking buyers of luxury automobiles;        Fashion conscious clothes buyers.        
As may be seen in these examples, grouping of potential customers using attitudinal characteristics and definitions results in segments defined by more than mere demographics and the like. For example, rather than creating a group of potential luxury car buyers based on demographic information like income and past purchases, attitudinally-based segments look to the reasons for purchasing behavior. In this example, this results in a group of potential luxury car buyers that are grouped based on the reason for purchasing a luxury car (e.g., seeking prestige, professional appearance, etc.).
There is therefore a high level of interest in a customized target marketing system based on attitudinal dimensions. While other methods of attitudinal segmentation currently exist, there is need for a system that can also identify individual consumers who align attitudinally with the segment definitions. This combination enables direct-to-consumer contact with attitudinally relevant products and marketing offers. Moreover, such a system would be even more beneficial to marketers by having the capability to help determine the attitudinally-based segment definitions themselves, which may then be customized to each particular product being marketed.